Alignment Of Maps Research Articles

Fostering strategic synergy: Empirical insights on aligning innovation activities with competitive strategies

An effective innovation strategy is crucial for firm performance and national economic growth, yet many firms still face the challenge of misalignment between their innovation activities and strategies. While previous studies focused on the mediation role of innovation activities on strategy and performance, research is needed to examine the complex interdependencies between strategies and innovation activities to better understand their impact on firm performance.From a configurational perspective, this study investigates how different combinations of innovation activities and strategies influence firm performance. Twenty combinative effects of four categories of innovation activities and five strategic types on two firm performance measures (growth and profit) were explored, resulting in the creation of an innovation-strategy alignment map. This study contributes to innovation strategy literature by providing the alignment map, where four configurational clusters with equifinality were identified. It also contributes to competitive strategy literature by offering empirical verification for the “pure” versus “hybrid” debate and identifying two new types of hybrids with more and different strategic dimensions by using an empirical method, thus advancing the call for more detailed hybrid strategy research in this area. In addition, the innovation-strategy alignment map provides options to managers with different resources and capacities for effective planning.

Multi-label recognition in open driving scenarios based on bipartite-driven superimposed dynamic graph

The multi-label image recognition task is widely prevalent in real-world scenarios. Overcoming the issue of overlapping and densely packed objects in complex scenes is crucial. For instance, in traffic scenarios, there are overlaps and close proximity among pedestrians, various types of vehicles, and signage. However, a primary obstacle in leveraging label relationships to enhance image classification lies in effectively integrating label semantic topology information with the image data itself. In this paper, we propose a novel framework, the Bipartite-driven Superimposed Dynamic Graph Convolutional Network (Bi-SDNet), augmented with Mapping Alignment Module (MAM) and Semantic Decoupling Module(SDM). Our approach initially decomposes input features into representations capable of discerning category label semantics at multiple scales, facilitated by MAM and SDM modules. Furthermore, through the meticulously designed Superimposed Dynamic Graph, we adeptly capture content-aware category relationships for each image, effectively modeling the relationships between these representations for the final recognition task. We conducted extensive experiments on publicly available benchmark datasets and the traffic scene dataset WZ-traffic. The model achieved an impressive 87.5% mean average precision (mAP) on the MS-COCO dataset and a commendable 91% mAP on the WZ-traffic dataset. Our research introduces novel techniques and significant breakthroughs in this field, furnishing powerful tools for enhancing model performance.

Automated high-precision alignment of geologic maps with well orientation

The 3D reflection seismic interpretation entails the creation of structure maps of subsurface horizons across extensive areas, often spanning tens of thousands of square kilometers within sedimentary basins. To be correlated with well measurements, maps made from 3D reflection seismic are routinely assigned the correct depth at well control points. However, integrating orientation information measured from wells remains a challenge due to the absence of upscaling guidelines. We address the scale-dependent nature of geologic structure and well orientation information in subsurface mapping, particularly focusing on integrating kilometer-scale seismic interpretation with centimeter-scale well-measured dip and azimuth data. Our pioneering methodology streamlines this integration process through three essential steps: first, transforming well dip and azimuth into a gradient representation; second, smoothly extending gradient differences between the map and well within a user-defined radius of influence; and third, constructing an updated horizon map by bicubic spline interpolation to ensure automated and high-precision alignment. In addition, a structure-oriented interpolation technique is introduced to preserve faults and local structures during orientation correction. Application of the methodology to a 3D reflection seismic horizon demonstrates its effectiveness in automating the complex task of tying subsurface maps to well orientation information. This not only reduces the need for manual interventions but also introduces a new perspective on subsurface mapping. It provides a robust framework that enhances the accuracy and reliability of geologic interpretation, offering significant advancements beyond previous efforts in the literature.

High-quality reference genome decoding and population evolution analysis of prickly Sechium edule

Sechium edule (chayote) is an important vegetable crop belonging to the Cucurbitaceae family. To decipher the chayote genome, a high-quality chromosome-level chayote genome was obtained by genome sequencing and bioinformatic analysis. The total length was 612.91 Mb, and 25 755 genes were detected in the chayote genome. The contig N50 was more than 20.01 Mb, and the scaffold N50 was over 47.11 Mb. Of the genome, 60.35% were composed of repetitive sequences, and 31.18% of genome sequences belonged to long-terminal repeats. A global alignment of homologous regions in chayote and other Cucurbitaceae plant genomes was constructed using grape as a reference. Based on this genome-wide and global alignment map, researchers can easily identify homologous collinear genes of the studied genomes in most Cucurbitaceae species. Twenty-five chayote accessions were divided into two subgroups based on phylogenetic tree, population structure analysis, and principal component analysis using genome re-sequencing data. The chayote genome, re-sequencing dataset, and comprehensive genomic analysis will accelerate comparative and functional genomic analysis of chayote and other Cucurbitaceae species in the future.

Streamlined jet tagging network assisted by jet prong structure

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.

An algorithmic approach to detect generalization in sketch maps from sketch map alignment.

Sketch maps are valuable tools used across various disciplines including spatial cognition, environmental psychology, and spatial reasoning. A common approach to evaluate sketch maps in research is to align and compare them with metric maps. However, sketch maps are highly abstract and contain generalized information causing difficulty in their alignment. Current approaches to study sketch maps cannot handle generalized information. They require a one-on-one correspondence between features in the metric map and features in the sketch map. But memory is often generalized. This paper makes two contributions to the research on sketch maps: (i) we present an algorithmic approach to detect generalization in sketch maps (ii) we present an online tool that creates a generalized metric map corresponding to features in sketch maps. Previously, we identified nine types of generalization in sketch maps. In this paper, we develop formal operators to detect these generalizations and implement them as an online tool. We evaluated our algorithm with a set of 11 sketch maps containing 84 instances of generalization. The results indicated that our algorithm consistently detects instances of generalization in sketch maps.

Novel Approaches for Aligning Geospatial Vector Maps

Abstract. The surge in data across diverse fields presents an essential need for advanced techniques to merge and interpret this information. With a special emphasis on compiling geospatial data, this integration is crucial for unlocking new insights from geographic data, enhancing our ability to map and analyze trends that span across different locations and environments with more authenticity and reliability. Existing techniques have made progress in addressing data fusion; however, challenges persist in fusing and harmonizing data from different sources, scales, and modalities. This research presents a comprehensive investigation into the challenges and solutions in vector map alignment, focusing on developing methods that enhance the precision and usability of geospatial data. We explored and developed three distinct methodologies for polygonal vector map alignment: ProximityAlign, which excels in precision within urban layouts but faces computational challenges; the Optical Flow Deep Learning-Based Alignment, noted for its efficiency and adaptability; and the Epipolar Geometry-Based Alignment, effective in data-rich contexts but sensitive to data quality. In practice, the proposed approaches serve as tools to benefit from as much as possible from existing datasets while respecting a spatial reference source. It also serves as a paramount step for the data fusion task to reduce its complexity.

Integrating phylogenies into single-cell RNA sequencing analysis allows comparisons across species, genes, and cells.

Comparisons of single-cell RNA sequencing (scRNA-seq) data across species can reveal links between cellular gene expression and the evolution of cell functions, features, and phenotypes. These comparisons evoke evolutionary histories, as depicted by phylogenetic trees, that define relationships between species, genes, and cells. This Essay considers each of these in turn, laying out challenges and solutions derived from a phylogenetic comparative approach and relating these solutions to previously proposed methods for the pairwise alignment of cellular dimensional maps. This Essay contends that species trees, gene trees, cell phylogenies, and cell lineages can all be reconciled as descriptions of the same concept-the tree of cellular life. By integrating phylogenetic approaches into scRNA-seq analyses, challenges for building informed comparisons across species can be overcome, and hypotheses about gene and cell evolution can be robustly tested.

An optimized multisource acquisition and registration workflow for imaging guided ventricular tachycardia ablation

Abstract Background Catheter ablation is a cornerstone treatment for scar-related ventricular arrhythmias arising in patients affected by structural heart disease. Imaging integration with electroanatomical mapping (EAM) suites for ventricular tachycardia (VT) ablation procedural guidance is an established step for advanced substrate characterization and treatment. Purpose To compare and validate different approaches for intraprocedural multisource imaging registration and integration with EAM suites for VT ablation guidance. Methods Thirty consecutive patients prospectively enrolled in the ongoing VOYAGE clinical trial for imaging guided/aided VT ablation at our center were retrospectively analyzed. Multidetector computed tomography (MDCT) and late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging was performed before procedure. MDCT and LGE-CMR images were both acquired during end-expiratory breath hold. Same cardiac cycle phase images were used. Wide band LGE-CMR was acquired in case of a cardiac implantable electronic device was present to reduce artifacts. Images were imported in ADAS3D software. MDCT and CMR images were manually registered using established anatomical landmarks (left ventricle outflow tract, left ventricle apex, papillary muscles, left and right ventricle junction), were then respectively processed for aortic/ventricular anatomical reconstruction and myocardial characterization and then imported in CARTO3 electroanatomical mapping suite for procedural guidance. Registration between EAM and imaging was either performed with ascending aorta fast anatomical mapping (FAM) and 3D landmark alignment with aortic MDCT or via intracardiac echocardiography (ICE) left ventricle (LV) chamber segmentation and 3D landmark alignment with ventricular MDCT. ICE segmentation was also obtained for every case regardless of aortic FAM. Surface match between ICE and MDCT LV chamber reconstructions was calculated for method comparison. Results All 30 patients had an ICE segmentation of the LV, with a mean of 15,66±4,89 contours. Aortic FAM was obtained and used for aortic MDCT registration in 10 subjects. LV ICE was instead used for imaging alignment in 20 subjects. When compared to aortic FAM, LV ICE to LV MDCT alignment achieved lower average surface distance (3,86±1,41 mm vs 8,884±6,77 mm; p<0,001), lower minimum surface distance (0,002±0,03 vs 0,07±0,06; p= 0,001) and lower maximum surface distance (14,16±4,37 vs 22,93±11,10; p<0,001). Conclusion An optimized workflow consisting of ICE LV chamber segmentation was superior in terms of 3D surface distance to aortic FAM reconstruction to obtain reliable multisource imaging registration for MDCT/CMR guided/aided VT ablation.

ENHANCING 3T RETINOTOPIC MAPS USING DIFFEOMORPHIC REGISTRATION.

Retinotopic mapping aims to uncover the relationship between visual stimuli on the retina and neural responses on the visual cortical surface. This study advances retinotopic mapping by applying diffeomorphic registration to the 3T NYU retinotopy dataset, encompassing analyze-PRF and mrVista data. Diffeomorphic Registration for Retinotopic Maps (DRRM) quantifies the diffeomorphic condition, ensuring accurate alignment of retinotopic maps without topological violations. Leveraging the Beltrami coefficient and topological condition, DRRM significantly enhances retinotopic map accuracy. Evaluation against existing methods demonstrates DRRM's superiority on various datasets, including 3T and 7T retinotopy data. The application of diffeomorphic registration improves the interpretability of low-quality retinotopic maps, holding promise for clinical applications.

Unveiling Enhanced PEC Water Oxidation: Morphology Tuning and Interfacial Phase Change in α-Fe2O3@K-OMS-2 Branched Core-Shell Nanoarrays.

A simple fabrication method that involves two steps of hydrothermal reaction has been demonstrated for the growth of α-Fe2O3@K-OMS-2 branched core-shell nanoarrays. Different reactant concentrations in the shell-forming step led to different morphologies in the resultant composites, denoted as 0.25 OC, 0.5 OC, and 1.0 OC. Both 0.25 OC and 0.5 OC formed perfect branched core-shell structures, with 0.5 OC possessing longer branches, which were observed by SEM and TEM. The core K-OMS-2 and shell α-Fe2O3 were confirmed by grazing incidence X-ray diffraction (GIXRD), EDS mapping, and atomic alignment from high-resolution STEM images. Further investigation with high-resolution HAADF-STEM, EELS, and XPS indicated the existence of an ultrathin layer of Mn3O4 sandwiched at the interface. All composite materials offered greatly enhanced photocurrent density at 1.23 VRHE, compared to the pristine Fe2O3 photoanode (0.33 mA/cm2), and sample 0.5 OC showed the highest photocurrent density of 2.81 mA/cm2. Photoelectrochemical (PEC) performance was evaluated for the samples by conducting linear sweep voltammetry (LSV), applied bias photo-to-current efficiency (ABPE), electrochemical impedance spectroscopy (EIS), incident-photo-to-current efficiency (IPCE), transient photocurrent responses, and stability tests. The charge separation and transfer efficiencies, together with the electrochemically active surface area, were also investigated. The significant enhancement in sample 0.5 OC is ascribed to the synergetic effect brought by the longer branches in the core-shell structure, the conductive K-OMS-2 core, and the formation of the Mn3O4 thin layer formed between the core and shell.

Self-supervised learning for RGB-D object tracking

Recently, there has been a growing interest in RGB-D object tracking thanks to its promising performance achieved by combining visual information with auxiliary depth cues. However, the limited volume of annotated RGB-D tracking data for offline training has hindered the development of a dedicated end-to-end RGB-D tracker design. Consequently, the current state-of-the-art RGB-D trackers mainly rely on the visual branch to support the appearance modelling, with the depth map utilised for elementary information fusion or failure reasoning of online tracking. Despite the achieved progress, the current paradigms for RGB-D tracking have not fully harnessed the inherent potential of depth information, nor fully exploited the synergy of vision-depth information. Considering the availability of ample unlabelled RGB-D data and the advancement in self-supervised learning, we address the problem of self-supervised learning for RGB-D object tracking. Specifically, an RGB-D backbone network is trained on unlabelled RGB-D datasets using masked image modelling. To train the network, the masking mechanism creates a selective occlusion of the input visible image to force the corresponding aligned depth map to help with discerning and learning vision-depth cues for the reconstruction of the masked visible image. As a result, the pre-trained backbone network is capable of cooperating with crucial visual and depth features of the diverse objects and background in the RGB-D image. The intermediate RGB-D features output by the pre-trained network can effectively be used for object tracking. We thus embed the pre-trained RGB-D network into a transformer-based tracking framework for stable tracking. Comprehensive experiments and the analysis of the results obtained on several RGB-D tracking datasets demonstrate the effectiveness and superiority of the proposed RGB-D self-supervised learning framework and the following tracking approach.

No Head Left Behind – Multi-Head Alignment Distillation for Transformers

Knowledge distillation aims at reducing model size without compromising much performance. Recent work has applied it to large vision-language (VL) Transformers, and has shown that attention maps in the multi-head attention modules of vision-language Transformers contain extensive intra-modal and cross-modal co-reference relations to be distilled. The standard approach is to apply a one-to-one attention map distillation loss, i.e. the Teacher's first attention head instructs the Student's first head, the second teaches the second, and so forth, but this only works when the numbers of attention heads in the Teacher and Student are the same. To remove this constraint, we propose a new Attention Map Alignment Distillation (AMAD) method for Transformers with multi-head attention, which works for a Teacher and a Student with different numbers of attention heads. Specifically, we soft-align different heads in Teacher and Student attention maps using a cosine similarity weighting. The Teacher head contributes more to the Student heads for which it has a higher similarity weight. Each Teacher head contributes to all the Student heads by minimizing the divergence between the attention activation distributions for the soft-aligned heads. No head is left behind. This distillation approach operates like cross-attention. We experiment on distilling VL-T5 and BLIP, and apply AMAD loss on their T5, BERT, and ViT sub-modules. We show, under vision-language setting, that AMAD outperforms conventional distillation methods on VQA-2.0, COCO captioning, and Multi30K translation datasets. We further show that even without VL pre-training, the distilled VL-T5 models outperform corresponding VL pre-trained VL-T5 models that are further fine-tuned by ground-truth signals, and that fine-tuning distillation can also compensate to some degree for the absence of VL pre-training for BLIP models.

Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research.

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

A closer look at reinforcement learning-based automatic speech recognition

Reinforcement learning (RL) has demonstrated effectiveness in improving model performance and robustness for automatic speech recognition (ASR) tasks. Researchers have employed RL-based training strategies to enhance performance beyond conventional supervised or semi-supervised learning. However, existing approaches treat RL as a supplementary tool, leaving the untapped potential of RL training largely unexplored. In this paper, we formulate a novel pure RL setting where an ASR model is trained exclusively through RL via human feedback metrics, e.g., word error rate (WER) or binary reward. This approach promises to significantly simplify the annotation process if we could replace the conventional onerous annotation with a single numeric value in a human–computer interaction (HCI) way. Our experiments demonstrate the feasibility of this new setting and also identify two main inherent issues in conventional RL-based ASR training that may lead to performance degradation: (1) the mismatch issue between the action and reward has been commonly overlooked in Connectionist Temporal Classification (CTC) based models, which is attributed to the inherent CTC alignment mapping issue; (2) the classic exploration–exploitation trade-off still exists in the sampling stage of RL-based ASR, and finding the balance between them becomes a challenge. To address these issues, we first propose a new RL-based approach named CTC-aligned Policy Gradient (CTC-PG), which provides a unified formulation for different sampling strategies and alleviates the mismatch issue of action and reward in CTC-based models. Moreover, we propose Focal Sampling to balance the trade-off between exploration and exploitation with a flexible temperature parameter. Experiment results on LibriSpeech dataset showcase the effectiveness and robustness of our methods by harnessing the full potential of RL in training ASR models.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

A Majorization-Minimization Algorithm for Neuroimage Registration.

Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.

A framework to improve the alignment of individual cytoarchitectonic maps of the Julich-Brain atlas using cortical folding landmarks.

The segregation of the cortical mantle into cytoarchitectonic areas provides a structural basis for the specialization of different brain regions. In vivo neuroimaging experiments can be linked to this postmortem cytoarchitectonic parcellation via Julich-Brain. This atlas embeds probabilistic maps that account for inter-individual variability in the localization of cytoarchitectonic areas in the reference spaces targeted by spatial normalization. We built a framework to improve the alignment of architectural areas across brains using cortical folding landmarks. This framework, initially designed for in vivo imaging, was adapted to postmortem histological data. We applied this to the first 14 brains used to establish the Julich-Brain atlas to infer a refined atlas with more focal probabilistic maps. The improvement achieved is significant in the primary regions and some of the associative areas. This framework also provides a tool for exploring the relationship between cortical folding patterns and cytoarchitectonic areas in different cortical regions to establish new landmarks in the remainder of the cortex.

Comparison of the DNBSEQ platform and Illumina HiSeq 2000 for bacterial genome assembly

The Illumina HiSeq platform has been a commonly used option for bacterial genome sequencing. Now the BGI DNA nanoball (DNB) nanoarrays platform may provide an alternative platform for sequencing of bacterial genomes. To explore the impact of sequencing platforms on bacterial genome assembly, quality assessment, sequence alignment, functional annotation, mutation detection, and metagenome mapping, we compared genome assemblies based on sequencing of cultured bacterial species using the HiSeq 2000 and BGISEQ-500 platforms. In addition, simulated reads were used to evaluate the impact of insert size on genome assembly. Genome assemblies based on BGISEQ-500 sequencing exhibited higher completeness and fewer N bases in high GC genomes, whereas HiSeq 2000 assemblies exhibited higher N50. The majority of assembly assessment parameters, sequences of 16S rRNA genes and genomes, numbers of single nucleotide variants (SNV), and mapping to metagenome data did not differ significantly between platforms. More insertions were detected in HiSeq 2000 genome assemblies, whereas more deletions were detected in BGISEQ-500 genome assemblies. Insert size had no significant impact on genome assembly. Taken together, our results suggest that DNBSEQ platforms would be a valid substitute for HiSeq 2000 for bacterial genome sequencing.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.